With the development of Internet technology, the Internet has become an important means for network users to obtain information and resources. Under normal circumstances, a network user usually surfs one or more favorite websites (e.g., website A) and rarely visits other websites (e.g., website B). As a result, the website A includes records of activity data (e.g., searching, browsing, collecting and purchasing information of the website A) associated with the network user while website B does not include any record of activity data associated with this network user.
An example is an e-commerce website. E-commerce websites may be divided into three categories based on customer groups that are primarily served: B2B (business to business) type e-commerce websites, of which parties of transactions are both businesses; B2C (business to customer) type e-commerce websites, through which businesses provide online purchase services to individual customers; and C2C (customer to customer) type e-commerce websites, of which buying users are normally individual customers and selling users are primarily individuals with some being small-scale businesses. Under normal circumstances, these three types of e-commerce websites are operated by different network operators, and therefore are mutually independent of one another. Data of these websites is also independent of one another.
When a user of the website A browses a product, products that may be of interest to the user and products that are related to the currently browsed product are recommended and displayed to the user on a web page thereof to shorten a search path of products desired by the user and improve an efficiency of online shopping of the user on the website A. Existing Internet e-commerce websites mostly adopt intelligent recommendation systems to allow a user to see popular products on a web page thereof upon login, and obtain information of other products that are related to products purchased by the user.
Two methods of implementing main operations of the intelligent recommendation systems exist. One is to present products to a user of a website based on specific activity records of the user. Specifically, this method includes: recording, by the website, historical activities such as searching, browsing, collecting or purchasing of a product done by a certain user, and determining needs and product preferences of the user based on these historical activities using a predetermined algorithm, i.e., presenting relevant product information to the user according to the user's attention with respect to certain products that is reflected by the past activities of the user. Alternatively, relevant products corresponding to products that are currently drawing a great deal of attention are presented to the user based on characteristics of group activities associated with multiple users. Alternatively, products which have drawn attention from many similar user groups are presented to related users to perform targeted presentation of products for users of those groups.
Another implementation method used by existing intelligent recommendation systems includes: displaying, on a web page currently viewed by a user, products that have high click rates, high transaction volumes or high quality based on businesses that have been registered by and are of interest to the user or businesses that have been clicked relatively frequently by the user. This resolves to a certain extent the failure of a new user or a user having limited activity data to obtain further product information.
Nevertheless, no matter which implementation method is adopted by an existing recommendation system, displaying is basically conducted from within a website. Specifically, a server of the website displays products that are included in that website to registered users of that website. However, the registered users of that website desire not only products of that website but also products of other websites. Consider a user of a C2C website as an example. When the user of the C2C website needs to obtain information of a desired product from a B2B website, the user of the C2C website is required to further login or register at the B2B website to obtain the information of the desired product. User registration of a website not only takes up time, but also increases number of interactions between servers and affects efficiency of displaying products to other users due to frequent logins to the website. These problems inevitably lead to frequent or redundant responses of the servers to user requests, resulting in further reduction in processing speed and processing performance of the servers.
In short, a technical problem that is urgently needed to be resolved by one skilled in the art is how to provide an innovative method of displaying cross-website information in order to solve the problem of reduction in processing speed and processing performance of servers with respect to efficiency of displaying products when the products are displayed in existing technologies.